The mass execution of analytical models across many dimensions of data to predict optimal transaction decisions is a computationally intensive and data intensive job that cannot be feasibly implemented in real-time on traditional computing systems. As a result, current decisioning systems require parties to iterate over potential transactions and transaction structures with only a vague sense of direction. Moreover, the more negotiable parameters that are added to a proposed transaction structure, the more exacerbated the shortcomings of existing decisioning systems become.
In view of these and other shortcomings and problems with traditional decisioning systems, improved systems and techniques for conducting mass execution of analytical models across many dimensions of data are desired in order to predict optimal transaction decisions in real-time.